Jianquan Ouyang1, Yue Zhang1, Kun Fang1,2,*, Tianming Liu3, Xiangyu Pan2
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1593-1610, 2022, DOI:10.32604/cmc.2022.025072
- 24 February 2022
Abstract Cryo-Electron Microscopy (Cryo-EM) images are characterized by the low signal-to-noise ratio, low contrast, serious background noise, more impurities, less data, difficult data labeling, simpler image semantics, and relatively fixed structure, while U-Net obtains low resolution when downsampling rate information to complete object category recognition, obtains high-resolution information during upsampling to complete precise segmentation and positioning, fills in the underlying information through skip connection to improve the accuracy of image segmentation, and has advantages in biological image processing like Cryo-EM image. This article proposes A U-Net based residual intensive neural network (Urdnet), which combines point-level and More >
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